Tracking via deep neural networks (DNNs) is a type of machine learning (ML), and has advantages over other tracking methods such as using the classical Kalman filter and its known extensions. Typically, tracking using DNN (also referred to as deep tracking neural networks or simply deep tracking) require the DNN to be trained based on training datasets. These datasets may be annotated to contain object labels and occasionally annotated manually to apply tracking identifiers (IDs) to tracked objects. However, datasets typically are not annotated with any other temporal characteristics of the objects. The lack of massive datasets with such annotations to train the DNN is likely to mean that the resulting deep tracking algorithm would not perform well in practical real-life applications, such as in an autonomous vehicle where tracking multiple objects with different motion models is important.
The way in which most DNN tracking algorithms are trained is usually lacking in fully observed temporal behavior of the objects in the dataset, and is also usually limited in the amount of datasets that have annotations related to tracks. The cost of annotating data manually with high detail (e.g., including target IDs and track types) is high, and different track management rules are typically difficult for a human to follow.